Within the competitive forging environment, forgers face even greater pressures to find new ways to reduce scrap, lower costs, and increase the quality of their parts. While traditional optimization processes could be time-consuming or have unclear results, the digital age provides new tools for the forging optimization process.
For many forgers, seeing examples of other manufacturers who have undergone the same improvement process with great results is the best proof of why they should consider a forging optimization process themselves.
The Problems With the Traditional Forging Optimization Process
The traditional forging optimization process is incredibly labor-intensive and requires many iterations to produce meaningful results. To understand the impact of necessary changes, only one variable can be changed between iterations. These changes can vary from a change in geometry, a process parameter, or a change in the material.
After the change, the forging process must be performed again, and then the finished part can be compared to an earlier part. Hopefully, improvements can be seen, but the traditional forging optimization process must often continue for many iterations.
While improvements can certainly be made through this process, setup time, manpower, and cost will often limit the impact of the forging optimization process. Each iteration simply costs too much to justify more than a few iterations. Each floor trial means time, money, and resources not being used to produce finished goods.
Why Optimization Is Crucial for Reducing Costs
While traditional optimization is labor-intensive and can be too costly to produce optimal results, computer simulation technology has led to vast improvements. With forging simulation software, large sets of simulations can be outlined and performed automatically with much less overhead and time required.
The results of these simulations are also processed automatically and analyzed for statistical patterns, helping engineers determine the best solutions for a more efficient forging process.
Simulation allows engineers to not only make improvements to the forging process but also to understand where and how defects might be occurring. Minimizing defects and optimizing the forging process used to require many expensive floor trials and forgers with decades of expertise. Modern simulation software reduces the time requirements for process optimization while allowing less experienced forgers the tools to make those same improvements.
The software gives forgers much deeper insight into how the parts are being made. Understanding how to prevent defects while optimizing the forging process results in faster times to market, fewer floor trials, and less wasted material in the forging process. With simulation software, the forging optimization process occurs much faster with lower cost and a higher quality finished part.
A Case Study: Circular Flange Optimization
The Automotive Research Association of India (ARAI) wanted to perform a forging optimization process on a particularly tricky part: a circular flange with defects occurring in multiple process stages.
After heating, the flange was processed in two stages, starting with a blocker using a hammer followed by a combined operation of trimming/piercing/drawing with a hydraulic press. In the first stage, the circular flange would suffer from under-filling, while undesired curvature and tearing would occur later. Reduced tool life was a problem in both stages as well.
The forging optimization process started with simulating the current process using the FORGE™ simulation software. The simulation was set up to model the process ARAI was currently using, and the model's results predicted the under-filling, tearing, and undesired curvature seen in the actual forging process. Once the model was validated, process improvements could begin.
Blocker Stage Optimization
The engineers began optimizing the blocker stage to eliminate the under-filling defect noted in the actual forging process. With the FORGE™ simulation, engineers noted that more material was gathering toward the center of the flange and not filling the corners. This indicated improper die design. The team also noted that the initial billet size was inadequate. With these parameters identified, the team could iterate through simulations with changes to both the billet size and shape as well as the design of the die.
The die shape was changed in successive iterations, and the engineers saw the impact of draft angles, inclinations, fillets, curvatures, and other features on the blocker stage. The billet size was also increased, and the cross-section changed from rectangular to circular. With the FORGE™ software, many iterations could be performed quickly and with no wasted floor trials.
Ultimately, the team was satisfied that changes to the die design and billet suggested the under-filling defect had been corrected.
Combined Operation Optimization
Having optimized the blocker stage, the combined operation stage was then optimized to complete the forging optimization process. In the combined operation, the team noted that less material was getting pierced from the central area of the component.
The design of the die was causing more material to flow outside the area of interest leading to more waste. The punch was also not providing proper curvature, which increased stress concentrations on the hub. These stress concentrations led to crack initiation and made tearing more likely while reducing tool life.
Armed with the FORGE™ simulation software, the team began modifying the design of the die and iterating through simulations. Proper fillets and curvatures were used to minimize the generation of stress concentrations while properly directing the flow of material. These modifications not only led to a reduction in tearing and undesired curvature but also reduced the press load requirement by almost two-thirds.
Results of the Forging Optimization Process
Having completed the computer simulations, the ARAI team made the required tooling modifications. The results were just as impressive as the FORGE™ software predicted. The team successfully modeled the current process and learned why the defects occurred.
Through iterative design changes, the team found proposed changes that would improve the forging process. After making those changes to the real process, all forging defects were removed using the modified forging process. After floor trial validations, the ARAI team concluded that the forging optimization process had resulted in a reduction in time, cost, energy consumption, and waste.
Reduce Costs With Forging Process Optimization
By reducing the number of floor trials required and increasing the number of potential iterations, forging simulation software can drastically improve the quality of forged parts while reducing manpower, material waste, and labor required.
Modern simulation tools give all of the advantages of forging process optimization without the labor required to perform the process traditionally. Understanding the tools available allows forgers to remain competitive while driving costs down and improving quality.